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    MLOPS Engineer - Bengaluru, India - Wipro

    Wipro
    Wipro Bengaluru, India

    Found in: Appcast Linkedin IN C2 - 1 week ago

    Wipro background
    Description

    Role: MLOPS Engineer

    Exp: 5-8 yrs

    Location: Anywhere in India

    Notice Period: Immediate to 15 Days

    Primary Skills: Python, Docker, CICD process, GIT

    Optional Skills: MLFlow, Airflow, LLM, Kubernetes

    Job Description:

    AI Platform Engineering : Build an highly available AI Ecosystem.

    Build technology centric solutions that accelerate the development of AI and ML capabilities across the company leveraging Kubernetes.

    Role consist in enabling Multi cluster architecture acting as Cloud native ecosystem to deploy AI Product . You'll designing, coding and testing/debugging applications, as well as documenting procedures for use and quality improvements.

    As Example you would participate / lead build of AI compute cluster deployment ( incl GPU Farm) , Feature store framework, model registry .

    This suppose to be writing, documenting and maintaining high quality code with object-oriented programming languages such as Python, C# Cloud-native computing; environment ( Kubernetes) and Micro Services Architecture.

    You'll Leverage a range of applicable technologies (e.g., programming languages, compilers, assemblers, debuggers, other tools) for products used in local, networked, storage enhancements and/or new systems.

    Generate technical documentation as well as unit and functional tests. Assist in training, work assignment, and mentoring of developers and other engineers.

    Support and engage with internal team Partner to ensure high availability of the solution.

    Automating AI/ML model deployment and Setting up monitoring for the ML pipeline

    Automating CI/CD pipelines to account for data, code, and model changes

    Programming, working knowledge of machine learning algorithms and frameworks, and domain knowledge

    Querying and working with databases, testing ML models, Git and version control, frameworks like Flask, FastAPI

    Proficiency in tools such as Docker and Kubernetes

    Familiarity with experiment tracking frameworks such as MLflow

    Setting up and automating data pipelines using tools such as Airflow, Kafka amd RabbitMQ

    Providing best practices and executing POC for automated and efficient model operations at scale.

    Good to have hands-on experience using large foundation models (e.g. LLMs) and associated tool chains (e.g. langchain) and APIs to build applications, tools and workflows for production.


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